code: def forward(
      self, input: torch.Tensor, lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      time_reduction_out, time_reduction_lengths = self.time_reduction(
          input, lengths
      )
      input_linear_out = self.input_linear(time_reduction_out)
      x, lengths = self.conformer(
          input_linear_out, time_reduction_lengths
      )
      output_linear_out = self.output_linear(x)
      layer_norm_out = self.layer_norm(output_linear_out)
      return layer_norm_out, lengths
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 2 for calling the _ConformerEncoder obj's underlying member method code with example usage: obj.forward(input, lengths) and returns: layer_norm_out,  lengths